Seismic hazard analysis of Lucknow considering local and active seismic gaps

The Himalayas are one of very active seismic regions in the world where devastating earthquakes of 1803 Bihar–Nepal, 1897 Shillong, 1905 Kangra, 1934 Bihar–Nepal, 1950 Assam and 2011 Sikkim were reported. Several researchers highlighted central seismic gap based on the stress accumulation in central part of Himalaya and the non-occurrence of earthquake between 1905 Kangra and 1934 Bihar–Nepal. The region has potential of producing great seismic event in the near future. As a result of this seismic gap, all regions which fall adjacent to the active Himalayan region are under high possible seismic hazard due to future earthquakes in the Himalayan region. In this study, the study area of the Lucknow urban centre which lies within 350 km from the central seismic gap has been considered for detailed assessment of seismic hazard. The city of Lucknow also lies close to Lucknow–Faizabad fault having a seismic gap of 350 years. Considering the possible seismic gap in the Himalayan region and also the seismic gap in Lucknow–Faizabad fault, the seismic hazard of Lucknow has been studied based on deterministic and the probabilistic seismic hazard analysis. Results obtained show that the northern and western parts of Lucknow are found to have a peak ground acceleration of 0.11–0.13 g, which is 1.6- to 2.0-fold higher than the seismic hazard compared to the other parts of Lucknow.     

Liquefaction Resistance and Cyclic Response of Air Injected-Desaturated Sandy Soil

Geotechnical and Geological Engineering - Desaturation of in-situ saturated sandy soils is emerging as a new cost effective and environment friendly liquefaction mitigation technique. In the...     

Geotechnical aspects and ground response studies in Bhuj earthquake, India

The extent of damage and affected areas in Bhuj earthquake (26th January 2001) has provided a unique opportunity to evaluate a wide range of geotechnical issues. A large area in the Rann of Kutch experienced massive liquefaction resulting in ground subsidence and lateral flow. A large number of dams in the Kutch district suffered moderate to severe damages. Many buildings were damaged and collapsed in the city of Ahmedabad situated on the bank of the Sabarmati River. In this paper, the ground response studies at a site in Ahmedabad City along with observations of geotechnical aspects such as ground cracking, sand volcanoes and liquefaction of soils associated with the Bhuj earthquake are discussed. The ground response studies indicate that the varying degree of damage to multistorey buildings in Ahmedabad in the close proximity of Sabarmati river area was essentially due to the collapse and undesirable settlement of partly saturated silty sand deposits. Large settlements are attributed to amplification of the ground and the near resonance condition. This revised version was published online in July 2006 with corrections to the Cover Date.     

Numerical simulation of liquefaction behaviour of granular materials using Discrete Element Method

In this paper, numerical simulation of 3-dimensional assemblies of 1000 polydisperse sphere particles using Discrete Element Method (DEM) is used to study the liquefaction behaviour of granular materials. Numerical simulations of cyclic triaxial shear tests under undrained conditions are performed at different confining pressures under constant strain amplitude. Results obtained in these numerical simulations indicate that with increase in confining pressure there is an increase in liquefaction resistance.     

Numerical Simulation of Explosion in Twin Tunnel System

Rapid growth of urban population in Indian cities have led traffic congestion leading to demand for scientific utilization of underground space. Immediate underground level and deep level underground below the major arterial roads are the sustainable spaces available for meeting the demand of the future traffic/transport. Due to recent increased transit activities it has become one of the soft targets by terrorists or prone to catastrophic accidents in recent years which have increased the importance of rock structures study under explosive loading. In this paper, the response of a underground metro tunnel subjected dynamic loads have been investigated including explosive capacity (30 kg TNT), ground characteristics, liner thickness and blast pressure characteristics. Blast pressure representing CONWEP air blast loading model with positive over pressure phase was applied to lining of tunnel. A three dimensional explicit finite element method was used to analyze dynamic response and damage in twin tunnels of underground metro. It is found that liner of thickness 28 cm will start deforming at the explosive loading of more than 65 kg TNT.     

Effect of Slope on <Emphasis Type="Italic">p</Emphasis>-<Emphasis Type="Italic">y</Emphasis> Curves for Laterally Loaded Piles in Soft Clay

Pile foundations are often subject to lateral loading due to various forces on a variety of structures like high rise buildings, transmission towers, power stations, offshore structures and highway and railway structures. The present investigation is to study the effect of slopes on p-y curves (where p is the static soil reaction and y is the pile deflection) due to static lateral loading in soft clay (Consistency index I_c = 0.42). A series of laboratory model tests were carried out on the instrumented model pile on sloping ground (slopes of 1V:1H, 1V:1.5H, 1V:2H, 1V:3H and 1V:5H) and with varying embedment length to diameter ratio (L/D) of 20, 25 and 30. From the experimental results, the bending moment curves along the pile shaft are double differentiated to obtain the soil resistance (p) and double integrated to obtain the deflection (y) using curve fitting method. New p-y curves for piles located on crest of soft clay with different sloping ground surface under static lateral loading are developed. Moreover, the effect of sloping angles on proposed p-y curves was studied.     

Comprehensive seismic hazard assessment of Tripura and Mizoram states

Northeast India is one of the most highly seismically active regions in the world with more than seven earthquakes on an average per year of magnitude 5.0 and above. Reliable seismic hazard assessment could provide the necessary design inputs for earthquake resistant design of structures in this region. In this study, deterministic as well as probabilistic methods have been attempted for seismic hazard assessment of Tripura and Mizoram states at bedrock level condition. An updated earthquake catalogue was collected from various national and international seismological agencies for the period from 1731 to 2011. The homogenization, declustering and data completeness analysis of events have been carried out before hazard evaluation. Seismicity parameters have been estimated using G–R relationship for each source zone. Based on the seismicity, tectonic features and fault rupture mechanism, this region was divided into six major subzones. Region specific correlations were used for magnitude conversion for homogenization of earthquake size. Ground motion equations (Atkinson and Boore 2003; Gupta 2010) were validated with the observed PGA (peak ground acceleration) values before use in the hazard evaluation. In this study, the hazard is estimated using linear sources, identified in and around the study area. Results are presented in the form of PGA using both DSHA (deterministic seismic hazard analysis) and PSHA (probabilistic seismic hazard analysis) with 2 and 10% probability of exceedance in 50 years, and spectral acceleration (T = 0. 2 s, 1.0 s) for both the states (2% probability of exceedance in 50 years). The results are important to provide inputs for planning risk reduction strategies, for developing risk acceptance criteria and financial analysis for possible damages in the study area with a comprehensive analysis and higher resolution hazard mapping.     

Geotechnical considerations for the concept of coastal reservoir at Mangaluru to impound the flood waters of Netravati River

ABSTRACT

This paper explores the geotechnical feasibility for constructing coastal reservoir in Arabian Sea off Mangaluru coast. This envisages storing fresh water in a reservoir along the coast by building a sea dike to impound the flood waters of Netravati River. On one side, the dike will ensure the required quantity of freshwater flow from Netravati River to the reservoir without being drained to the sea. On the other side, the sea dike will prevent seawater from entering the reservoir, avoiding the salt contamination of the freshwater supply. Present study presents detailed investigation of the soil profiles of surrounding region of Mangaluru to explore the site condition at off Ullal beach. Lithological data on the Netravati estuary were also presented with key observation on the soil profiles in the area proposed for location of coastal reservoirs. The key finding of the study is that the region offshore of Ullal is devoid of sand and is comprised mainly of soft Silty clays. Lithological data of nine foundations at Netravati Bridge near Ullal are also presented in this paper. Based on the findings of geotechnical investigations, the paper concludes that construction of sea dike in Arabian Sea off Mangaluru coast is feasible.     

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sq⋅km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing ε-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability of RVM over the SVM model.